Page - V - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Contents About theSpecial IssueEditors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Preface to”Short-TermLoadForecastingbyArtificial IntelligentTechnologies” . . . . . . . . . ix Ming-WeiLi, JingGeng,Wei-ChiangHongandYangZhang HybridizingChaotic andQuantumMechanisms and Fruit FlyOptimizationAlgorithmwith LeastSquaresSupportVectorRegressionModel inElectricLoadForecasting Reprintedfrom:Energies2018,11, 2226,doi:10.3390/en11092226 . . . . . . . . . . . . . . . . . . . 1 YongquanDong,ZichenZhangandWei-ChiangHong AHybridSeasonalMechanismwithaChaoticCuckooSearchAlgorithmwithaSupportVector RegressionModel forElectricLoadForecasting Reprintedfrom:Energies2018,11, 1009,doi:10.3390/en11041009 . . . . . . . . . . . . . . . . . . . 23 AshfaqAhmad,NadeemJavaid,AbdulMateen,MuhammadAwaisandZahoorAliKhan Short-TermLoadForecasting inSmartGrids:AnIntelligentModularApproach Reprintedfrom:Energies2019,12, 164,doi:10.3390/en12010164 . . . . . . . . . . . . . . . . . . . . 44 SeonHyeogKim,GyulLee,Gu-YoungKwon,Do-InKimandYong-JuneShin DeepLearningBasedonMulti-DecompositionforShort-TermLoadForecasting Reprintedfrom:Energies2018,11, 3433,doi:10.3390/en11123433 . . . . . . . . . . . . . . . . . . . 65 Fu-ChengWangandKuang-MingLin Impacts of Load Profiles on the Optimization of Power Management of a Green Building EmployingFuelCells Reprintedfrom:Energies2019,12, 57,doi:10.3390/en12010057 . . . . . . . . . . . . . . . . . . . . 82 HabeeburRahman, IniyanSelvarasanandJahithaBegumA Short-TermForecastingofTotalEnergyConsumptionfor India-ABlackBoxBasedApproach Reprintedfrom:Energies2018,11, 3442,doi:10.3390/en11123442 . . . . . . . . . . . . . . . . . . . 98 JihoonMoon,YongsungKim,MinjaeSonandEenjunHwang HybridShort-TermLoadForecastingSchemeUsingRandomForestandMultilayerPerceptron Reprintedfrom:Energies2018,11, 3283,doi:10.3390/en11123283 . . . . . . . . . . . . . . . . . . . 119 MiguelLo´pez,CarlosSans,SergioValeroandCarolinaSenabre Empirical Comparison ofNeuralNetwork andAuto-RegressiveModels in Short-TermLoad Forecasting Reprintedfrom:Energies2018,11, 2080,doi:10.3390/en11082080 . . . . . . . . . . . . . . . . . . . 139 Marı´adelCarmenRuiz-Abello´n,AntonioGabaldo´nandAntonioGuillamo´n LoadForecastingforaCampusUniversityUsingEnsembleMethodsBasedonRegressionTrees Reprintedfrom:Energies2018,11, 2038,doi:10.3390/en11082038 . . . . . . . . . . . . . . . . . . . 158 GregoryD.Merkel,RichardJ.PovinelliandRonaldH.Brown Short-TermLoadForecastingofNaturalGaswithDeepNeuralNetworkRegression Reprintedfrom:Energies2018,11, 2008,doi:10.3390/en11082008 . . . . . . . . . . . . . . . . . . . 180 Fu-ChengWang,Yi-ShaoHsiaoandYi-ZheYang TheOptimizationofHybridPowerSystemswithRenewableEnergyandHydrogenGeneration Reprintedfrom:Energies2018,11, 1948,doi:10.3390/en11081948 . . . . . . . . . . . . . . . . . . . 192 v